基于解剖结构边缘增强的高清扩散MRI微结构成像OA

Diffusion magnetic resonance imaging is a crucial non-invasive technique for probing the microstructure of the human brain in vivo.Traditional hand-crafted and model-based tissue microstructure reconstruction methods typically require extensive diffusion gradient sampling,which is time-consuming and limits their clinical applicability.Recent advances in deep learning have shown great potential for microstructure estimation;however,accurately inferring tissue microstructure from clinically feasible diffusion magnetic resonance imaging scans remains challenging without appropriate constraints.In this paper,we propose a scalable and flexible framework compatible with diverse network architectures.By integrating macro-scale anatomical priors and cross-parameter mutual information from multiple diffusion models,together with total variation regularization,our approach achieves high-fidelity and efficient diffusion microstructure imaging.Experimental results demonstrate that the method significantly reduces scan time while maintaining—or even improving—the accuracy of microstructure estimation.